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# RWE Scientist Consultant

- **Company:** Simulstat Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Python (Programming Language), SAS (Software), SQL Databases
- **Published:** July 23, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=d7fc19db12c07874

## About the Role

* Advanced degree in epidemiology, biostatistics, health services research, or a related field (PhD, MD, PharmD, MPH, or MSc)
 * 8+ years of hands-on observational research experience in pharma, biotech, or HEOR consulting
 * Proven ability to program and run analyses independently, end-to-end, in claims/EHR/registry data (R preferred; SQL, SAS, or Python valued)
 * Solid grasp of epidemiologic methods and regulatory/HTA evidence expectations for RWE
 * Strong writing and communication skills, including scientific publication experience
 * Comfortable working autonomously with shifting priorities

Preferred:

 * Experience with AI-assisted coding tools (e.g., Claude Code)
 * Rare disease or specialty therapeutic area experience
 * Experience with external control arms or target trial emulation

## Description

We're hiring hands-on RWE Scientist Consultants for a growing pharma HEOR/RWE function supporting late-stage and launch-phase programs. This is an individual-contributor role - you'll personally build and run real-world studies, not just direct others. You'll take ownership of a disease-area data program and work closely with therapeutic area leadership to turn evidence questions into rigorous, decision-ready analyses using claims, EHR, and registry data., * Design and run observational studies (e.g., disease burden, treatment patterns, comparative effectiveness) using large healthcare datasets

 * Write the code yourself - primarily in R, with SQL/SAS/Python as needed against claims, EHR, and registry data - from cohort-building through final analysis
 * Develop protocols, analysis plans, and feasibility assessments, including confirming data availability before a study kicks off
 * Apply sound epidemiologic methods (causal inference, bias control) suited to observational data
 * Contribute findings to evidence dossiers, regulatory-facing materials, and publications (manuscripts, abstracts, conference presentations)
 * Occasionally review outsourced vendor analyses for quality - an as-needed part of the work, not a management responsibility


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